Sentiment Analysis machine learning system for text classification
The goal of this project was to design a sentiment analysis system that can classify text into positive or negative categories. Businesses can use such a system to monitor customer reviews, track social media feedback, and measure overall customer satisfaction in real time.
Customer feedback data is unstructured and difficult to analyze manually at scale. A robust automated system is required to preprocess text, extract features, train machine learning models, and deliver accurate predictions through an accessible interface.
Cleaned raw text using tokenization, stopword removal, and lemmatization. Applied TF-IDF vectorization to convert text into numerical features for modeling.
Implemented multiple machine learning models including RandomForest, XGBoost Classifier and Compared their performance and optimized hyperparameters for best accuracy.
Evaluated models using accuracy, precision, recall, and F1-score. Achieved over 90% accuracy with the optimized model and analyzed performance with a confusion matrix.
Instant classification of text into positive or negative sentiment.
Confusion matrix and metric reports to evaluate classifier performance.
Achieved 90+ accuracy using TF-IDF with XGBoost Classifier after hyperparameter tuning.
Cleaning steps like lemmatization and stopword removal boosted accuracy
XGBoost outperformed RandomForest in precision-recall comparison
The system design allows future extension to multi-class sentiment (positive, negative, neutral).
Developed an NLP-driven sentiment analysis system using Python and scikit-learn. Built preprocessing pipelines, compared models, and achieved over 90% accuracy. This project demonstrates practical application of machine learning and NLP in customer feedback analytics.